{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"id":"29Z_hW2j5IR5"},"outputs":[],"source":["# Install libraries\n","!pip install torch #pytorch\n","!pip install transformers #huggingface transformer\n","!pip install librosa #for managing audio files"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Z19bB73w8DWu"},"outputs":[],"source":["# Import libraries\n","import torch\n","import librosa\n","from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer #import Wav2Vec huggingface transformer\n","import os\n","import numpy as np\n","import librosa.display\n","import matplotlib\n","import pandas as pd"]},{"cell_type":"markdown","metadata":{"id":"hTPgm4JNBub3"},"source":["## Load Audio Data from gdrive"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ISpSKe_2Jjei"},"outputs":[],"source":["# Mount the drive that contains wav files\n","from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VNeSumbaAew6"},"outputs":[],"source":["# Find all file paths containing WAV files using librosa's utility function.\n","# Note: Files with a size less than 100KB (~ <2 seconds) were excluded in a previous step,\n","# as librosa.load cannot resample them to 16000Hz.\n","\n","\n","# Define the directory path where the WAV files are located.\n","files = librosa.util.find_files('path-containing-wav-files', ext='wav', recurse=False)\n","\n","# Convert the list of file paths to a NumPy array.\n","files = np.asarray(files)\n","\n","#len(files)\n","#files"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ms4OaUC5OT5L"},"outputs":[],"source":["# Load and resample all WAV files to 16,000 Hz using librosa.load.\n","\n","df=[]\n","for i in files:\n","  df.append(librosa.load(i, sr=16000))\n","  #df.append(librosa.load(i)) #it also works without resampling\n","\n","#print(df)\n","\n","librosa_values=pd.DataFrame(df, columns=['value', 'sr'])\n","#librosa_values\n","\n","# Extract the 'value' column from the DataFrame\n","#value = librosa_values.value\n","\n","#value"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":264,"referenced_widgets":["2f7d5132e747493aab33256282066848","5f4474d8868446f0b8615b917077fc83","0b49e4d1f7c84f4e99765e69e1cac519","6fc485fc423449ae80edbd498b64273c","fd7cafa276674806acb356cde426ce09","5ddc9eca3ea9467eafe441d2579a1c2d","057fbe1cd7de4b778091d8102d0d2957","dd48e76eead04a5a98925fd3a4e715b7","8be18642d4474588b46d5700ddc640f6","9253dd376d91434cb240b172df64ed5b","f7b6eb540dd842cebf99fd92ba3e89d0","3fb965e081a3439099ca99297d31e28f","73675cf8c1bd407bb493ac9ed995c487","bd3e40aecccd46839c965df6b68ad2a1","dfabfa8db34543f5b3511bd25a3a3629","c367ad118eba4c4aa14ae1c7afda9dca","a24037ad7643457aa4b0dcbd99baaaab","560e0eeeddb0465bb2c84e14381b2d3e","a8f662c27f064784b9458e5f0051bf36","e1344cab98374992b45e7a6a9970111c","a68fe1aae9d64b5987120b31af781d8d","4dc9e19b2cfd4792921a44b43efff1d8","0ef363121c8b4c64985a9373fbf62238","4703cda3a12a4cfa874a8b73c68c56de","82798a2c26344a29b6159ac6edcac726","4a5f131cf32c4e7b874f04fe46475bf5","716e35b24778417caa07d5e5723f4dc4","500cff2f29824551b1d3940b0c2eb1cd","e2932523c5554a9b8813466a4bd4c2da","55344f401d6f4758af88c8a43479253a","943127ee4a5a4772839f16ee6eabf259","31dc6d2a19214bfa8805d29b48d1842f","52ae4c6fd5e2444e941d6775dfe80470","d95c40d3fe7b4b78a2038be5c5217152","ad70c71d9c8f4b2dac4e6e492acbf748","0bdd514f14c64049bc57a1f75748d2d1","9adfe8467fca400192165aa46b3e5c40","fdb17e36c611455b947bef391553b5a7","d4c575deb83641f7a32cc80712b62767","ed88adb08c1a40069c21761c5d95706b","10ab06f8e3444c5b94b63a5f05446d5f","8b899a41d5564f208415430552061ac5","66bb2ea9d78447efbef8427b30fa64c4","fae298c0a44d45e3b405b8966a3311cc","728d4c474c0e4212baa143a7e60d3591","1698520edafb4e62ae9596af7f73a8d1","8852e0eddef345eeab03309d69d0b941","b3a4f18fbe44476cb2cadeb9c3adfc59","0f1b18a0c0cb49329c5d9c0cf071fb09","aea5a01d30224b9fafe4d734e8d94f62","ac49086c1f0b42b78ce8cf87afa54193","983744dc4a1b40d8b547403718b873f5","179a9a517eaa4cd59b32bc8754971d8b","c6ce38faa5d94079ba7b4130b773bc6b","c318fdbf6cac4efe894f4719add11eac"]},"executionInfo":{"elapsed":26890,"status":"ok","timestamp":1680438351727,"user":{"displayName":"Jan B.","userId":"11962631558800943951"},"user_tz":-120},"id":"VfvVtO6BABS2","outputId":"512cd6df-6f77-48c6-e9ba-127dd2af2a22"},"outputs":[],"source":["# Importing Wav2Vec pretrained model from HuggingFace\n","tokenizer =  Wav2Vec2Tokenizer.from_pretrained(\"facebook/wav2vec2-large-960h-lv60-self\") #(\"facebook/wav2vec2-base-960h\")\n","model = Wav2Vec2ForCTC.from_pretrained(\"facebook/wav2vec2-large-960h-lv60-self\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kK40bqNVB8MZ"},"outputs":[],"source":["# The next step is to process the input values by passing the audio (array) into a tokenizer.\n","# We want our tensors in PyTorch format, so we set return_tensors = \"pt\" which is PyTorch format.\n","\n","# Initialize an index for tracking progress.\n","input_values=[]\n","\n","# Initialize an index for tracking progress.\n","index = 0\n","\n","# Loop through the audio values in 'librosa_values'.\n","for i in librosa_values[\"value\"]:\n","  # Tokenize the audio and append the input values to 'input_values'.\n","\n","   input_values.append(tokenizer(i, return_tensors = \"pt\").input_values)\n","   # Print the index to track progress.\n","   print(index)\n","\n","   # Increment the index.\n","   index = index +1\n","\n","# 'input_values' now contains the tokenized input in PyTorch format."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"JENTNIUPD10R"},"outputs":[],"source":["# If an out-of-RAM error occurs when executing the next code chunk, save resulting tensors and data to files.\n","# You can continue with the following code chunks on a local machine.\n","\n","# Save the 'data' DataFrame to a pickle file named 'data.pkl'.\n","#data.to_pickle('data.pkl')\n","\n","# Save 'input_values' (tensors) to a PyTorch file named 'input_values.pt'.\n","#torch.save(input_values, 'input_values.pt')"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true},"id":"G5526OiEQK6V"},"outputs":[],"source":["# Store logits (non-normalized prediction values) for the input values.\n","\n","# Initialize an empty list to store the logits.\n","logits = []\n","\n","# Initialize an index for tracking progress.\n","index = 0\n","\n","# Use 'torch.no_grad()' to disable gradient calculation, as we are not training the model.\n","with torch.no_grad():\n","    # Loop through the input values and compute logits using the ASR model.\n","    for i in input_values:\n","        logits.append(model(i).logits)\n","\n","        # Print the index to track progress.\n","        print(index)\n","\n","        # Increment the index.\n","        index = index + 1\n","\n","# 'logits' now contains the non-normalized prediction values."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IHNUD4mwT0Jq"},"outputs":[],"source":["# Pass the logits (non-normalized prediction values) through softmax to get the predicted values.\n","\n","# Initialize an empty list to store the predictions.\n","prediction = []\n","\n","# Loop through the logits and compute predictions using the argmax operation.\n","for i in logits:\n","    prediction.append(torch.argmax(i, dim=-1))\n","\n","# 'prediction' now contains the predicted values after applying softmax.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2BHYXvmnT3UC"},"outputs":[],"source":["# Convert predicted values to text by passing them through the tokenizer's decode function.\n","\n","# Initialize an empty list to store the transcriptions.\n","transcription = []\n","\n","# Loop through the predictions and use the tokenizer's batch_decode to obtain transcriptions.\n","for i in prediction:\n","    transcription.append(tokenizer.batch_decode(i)[0])\n","\n","# 'transcription' now contains the text transcriptions.\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"2n4eChiG4fwd"},"outputs":[],"source":["# Merge the transcribed text ('strings') with a 'data' DataFrame by adding a new column named 'transcription'.\n","data=[]\n","data[\"transcription\"]=strings\n","data"]}],"metadata":{"colab":{"provenance":[{"file_id":"1u8jqpYgC6CPXxDRw3HK0YLL-ywLflfvU","timestamp":1657019927656}]},"gpuClass":"standard","kernelspec":{"display_name":"Python 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